2023
,
Liao, Brian Hsuan-Cheng
,
Cheng, Chih-Hong
,
Esen, Hasan
,
Knoll, Alois
As an emerging type of Neural Networks (NNs), Transformers are used in many domains ranging from Natural Language Processing to Autonomous Driving. In this paper, we study the robustness problem of Transformers, a key characteristic as low robustness may cause safety concerns. Specifically, we focus on Sparsemax-based Transformers and reduce the finding of their maximum robustness to a Mixed Integer Quadratically Constrained Programming (MIQCP) problem. We also design two pre-processing heuristics that can be embedded in the MIQCP encoding and substantially accelerate its solving. We then conduct experiments using the application of Land Departure Warning to compare the robustness of Sparsemax-based Transformers against that of the more conventional Multi-Layer-Perceptron (MLP) NNs. To our surprise, Transformers are not necessarily more robust, leading to profound considerations in selecting appropriate NN architectures for safety-critical domain applications.
2023
,
Pandharipande, Ashish
,
Cheng, Chih-Hong
,
Dauwels, Justin
,
Gurbuz, Sevgi Z.
,
Ibanez-Guzman, Javier
,
Li, Guofa
,
Piazzoni, Andrea
,
Wang, Pu
,
Santra, Avik
Automotive perception involves understanding the external driving environment as well as the internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving high levels of safety and autonomy in driving. This paper provides an overview of different sensor modalities like cameras, radars, and LiDARs used commonly for perception, along with the associated data processing techniques. Critical aspects in perception are considered, like architectures for processing data from single or multiple sensor modalities, sensor data processing algorithms and the role of machine learning techniques, methodologies for validating the performance of perception systems, and safety. The technical challenges for each aspect are analyzed, emphasizing machine learning approaches given their potential impact on improving perception. Finally, future research opportunities in automotive perception for their wider deployment are outlined.